Hepatic Gene Expression Analysis

22160 R for bio data science

Antoine Andréoletti, Olivier Gaufrès, Amy Surry, Lea Skytthe, and Trine Søgaard

Introduction

Aim: Investigating hepatic gene expression by comparing expression levels across:

- Healthy individuals - Patients with NAFLD - Patients with cirrhosis ### Data

![](doc/Pictures_for_presentation/liver_pic.png){style=“width:80%;”}

RNA-seq of human liver biopsies from patients with NAFLD, patients with cirrhosis, and healthy controls under both fasting and postprandial conditions
Meta data containing additional information about the patients

Data processing

Methods

Workflow Diagram

Results - Descriptive analysis

Aim: First analysis of our datasets

- Distributions are fairly equal throughout our data - Sick people are older on average - No significance in disease between men and women

![](results/EDA.png){style=“width:85%”}

Results - PCA/Heatmap

Some plots

Results - DEA

Compare gene expression levels between groups using DESeq2 Output: Global Mean - Fold-change - Adjusted-p-value

Results - GSEA

Aim: Investigating hepatic gene set enrichment

- C11 hepatocytes is a subtype of hepatocytes (1 of 3) - Hepatocytes produces hepatokines (hormone involved in metabolic regulation) - Some therapies try to promote hepatocyte regeneration

![](results/GSEA/GSEA_sick_vs_healthy_patients_fasting.png){style=“width:100%”}

Discussion / Conclusion

Key points
Count_data derived from micro-array
Troubles with DESeq2 package and dplyr
Key points